Mammography is considered to be the most important modality in breast cancer screening and diagnosis. In dense breasts, however, the process of detecting subtle signs of cancer such as architectural distortions, masses and asymmetries is hampered by their reduced contrast in dense breast tissue. Additionally, it has been observed that an increased density of the breast is linked to a higher risk of developing breast cancer (cf. Ursin, G., et al.: Greatly Increased Occurrence of Breast Cancers in Areas of Mammographically Dense Tissue. Breast Cancer Res 7(5) (2005) R605-R608). There has been significant work on the field of mammographic image enhancement (cf., for example, Chan, H., et al.: Digital Mammography: ROC Studies of the Effects of Pixel Size and Unsharp-Mask Filtering on the Detection of Subtle Microcalcifications. Investigative Radiology 22(7) (1987) 581-589; Laine, A., et al.: Mammographic Feature Enhancement by Multiscale Analysis. IEEE Transactions on Medical Imaging 13(4) (1994) 725-739; Morrow, W., et al.: Region-Based Contrast Enhancement of Mammograms. IEEE Transactions on Medical Imaging 11(3) (1992) 392-406; Pisano, E., et al.: Image Processing Algorithms for Digital Mammography: A Pictorial Essay1. Radiographics 20(5) (2000) 1479) and it has been shown that these techniques can partly improve the detectability of important features in mammographic screening.
Nowadays, manufacturers of digital mammography systems include their proprietary post-processing algorithms to enhance digital mammograms for diagnostic presentation, which gives these processed mammograms a unique appearance and contrast. In Chen, B., et al.: Comparison of Tissue Equalization and Premium View Post-Processing Methods in Full Field Digital Mammography. European Journal of Radiology (2009), the authors compared the diagnostic abilities of two post-processing methods provided by the GE Senographe DS System, premium view (PV) and tissue equalization (TE). Their study showed that PV provided better diagnostic information compared to TE, particularly for patients with malignancy in dense breast.
During screening or therapy, patients frequently undergo examinations with mammography systems of different manufacturers. In the process of screening, a patient's current mammograms are compared to the prior mammograms in order to aid detecting changes in breast morphology, which can be an indication of a growing lesion. In Snoeren, P. and Karssemeijer, N.: Gray-Scale and Geometric Registration of Full-Field Digital and Film-Screen Mammograms. Medical Image Analysis 11(2) (2007) 146-156, the authors presented a gray-scale and geometric registration of full-field digital “for processing” mammograms to film-screen mammograms based on a parametric model of the acquisition aspects. However, in a clinical setting the availability of “for processing” images is not always granted for a number of reasons including system restrictions and external image acquisition. It would therefore be desirable to be able to automatically homogenize “for presentation” mammograms acquired with different machines and/or treated with different post-processing algorithms as this would ease the diagnostic assessment of prior-current mammogram pairs.